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Implicit equilibrium models, i.e., deep neural networks (DNNs) defined by implicit equations, have been becoming more and more attractive recently.
Iterative algorithms for nonlinear operators
Hong-Kun Xu · 2002
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Viscosity approximation methods for nonexpansive mappings
Xu Hong-Kun · 2004
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Fixed point and continuation results for contractions in metric and gauge spaces
Marlene Frigon · 2007
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Convex analysis and monotone operator theory in Hilbert spaces , volume 408
Heinz H Bauschke, Patrick L Combettes, et al · 2011
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Feature selection via dependence maximization
Le Song, Alex Smola, Arthur Gretton, Justin Bedo, and Karsten Borgwardt · 2012
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Hyperparameter optimization with approximate gradient
Fabian Pedregosa · 2016
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Optnet: Differentiable optimization as a layer in neural networks
Brandon Amos and J Zico Kolter · 2017
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First-order methods in optimization
Amir Beck · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Regularizing cnns with locally constrained decorrelations
Pau Rodríguez, Jordi Gonzalez, Guillem Cucurull, Josep M Gonfaus, and Xavier Roca · 2017
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A first order method for solving convex bilevel optimization problems
Shoham Sabach and Shimrit Shtern · 2017
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Fully convolutional networks for semantic segmentation
Evan Shelhamer, Jonathan Long, and Trevor Darrell · 2017
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Feature incay for representation regularization
Yuhui Yuan, Kuiyuan Yang, and Chao Zhang · 2017
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
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The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, and Nathan Srebro · 2018
Emilien Dupont, Arnaud Doucet, and Yee Whye Teh · 2019
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Meta-learning with implicit gradients
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, and Sergey Levine · 2019
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Multiscale deep equilibrium models
Shaojie Bai, Vladlen Koltun, and J Zico Kolter · 2020
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A characterization of proximity operators
Rémi Gribonval and Mila Nikolova · 2020
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Training neural networks by lifted proximal operator machines
Jia Li, Mingqing Xiao, Cong Fang, Yue Dai, Chao Xu, and Zhouchen Lin · 2020
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Optimizing millions of hyperparameters by implicit differentiation
Jonathan Lorraine, Paul Vicol, and David Duvenaud · 2020
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Independently interpretable lasso: A new regularizer for sparse regression with uncorrelated variables
Masaaki Takada, Taiji Suzuki, and Hironori Fujisawa · 2018
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High-dimensional probability: An introduction with applications in data science , volume 47
Roman Vershynin · 2018
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Differentiable convex optimization layers
Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, and J Zico Kolter · 2019
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Learning representations for neural network-based classification using the information bottleneck principle
Rana Ali Amjad and Bernhard C Geiger · 2019
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Regularizing deep neural networks by enhancing diversity in feature extraction
Babajide O Ayinde, Tamer Inanc, and Jacek M Zurada · 2019
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Deep equilibrium models
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2019
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Dissecting neural odes
Stefano Massaroli, Michael Poli, Jinkyoo Park, Atsushi Yamashita, and Hajime Asma · 2020
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Implicit regularization in deep learning may not be explainable by norms
Noam Razin and Nadav Cohen · 2020
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Lipschitz bounded equilibrium networks
Max Revay, Ruigang Wang, and Ian R Manchester · 2020
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Monotone operator equilibrium networks
Ezra Winston and J Zico Kolter · 2020
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Deep stable learning for out-of-distribution generalization
Xingxuan Zhang, Peng Cui, Renzhe Xu, Linjun Zhou, Yue He, and Zheyan Shen · 2021
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